Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI

Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that obser...

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Published in:Frontiers in human neuroscience Vol. 17; p. 1180056
Main Authors: Syrov, Nikolay, Yakovlev, Lev, Miroshnikov, Andrei, Kaplan, Alexander
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 04-05-2023
Frontiers Media S.A
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Summary:Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain-computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation.
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Reviewed by: Xiaogang Chen, Chinese Academy of Medical Sciences and Peking Union Medical College, China; Erwei Yin, Tianjin Artificial Intelligence Innovation Center (TAIIC), China
Edited by: Floriana Pichiorri, Santa Lucia Foundation (IRCCS), Italy
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2023.1180056